import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import pymysql
import pymysql.cursors

class MysqlUtils(object):
    def __init__(self):
        self.conn = pymysql.connect(
            host='127.0.0.1',
            user="root",
            passwd="root",
            db="scenic",
            port=3306,
            charset="utf8"
        )
        
    def is_holiday(self,date):
        """是否节假日判断
        """
        if date in ['2024-09-03','2024-10-01','2024-10-02','2024-10-03','2024-10-04','2024-10-05','2024-10-06','2024-10-07','2025-01-01','2025-01-02','2025-01-03']:
            return 1
        return 0
    
    def get_scenic_data(self):
        """获取数据
        """
        cursor=self.conn.cursor(cursor=pymysql.cursors.DictCursor)
        
        sql="""
        SELECT DATE(g.create_time) as date, HOUR(g.create_time) as hour, count(*) as  count FROM order_user_gate_rel g WHERE HOUR(g.create_time) BETWEEN 6 and 23 and DATE(g.create_time) < '2025-01-01' GROUP BY date,hour
        """
        
        cursor.execute(sql)
        ret = cursor.fetchall()
        
        df=pd.DataFrame(ret)
        #print(df.head)
        #格式转换
        date_range = pd.date_range(start='2024-07-01',end='2024-12-31',freq='D')
        hours = range(6,24)
        full_index=pd.MultiIndex.from_product([date_range,hours],names=['date','hour'])
        #print(full_index)
        df_full = df.set_index(['date','hour']).reindex(full_index,fill_value=0).reset_index()
        #按天组织数据,每行包含18个小时的检票次数
        df_pivot= df_full.pivot(index = 'date',columns='hour',values='count')
        #orint(df_pivot)
        df_pivot['dow'] = df_pivot.index.dayofweek
        df_pivot['month'] = df_pivot.index.month
        #print(df_pivot)
        df_pivot['is_holiday'] = df_pivot.index.map(self.is_holiday)
        
        #对星期几和月份进行独热编码
        df_pivot = pd.get_dummies(df_pivot,columns=['dow','month'],dtype=int)
        
        #归一化小时检票列
        hours_columns = list(range(6,24))
        df_hours = df_pivot[hours_columns].copy()
        
        feature_colunms = [col for col in df_pivot.columns if col not in hours_columns]
        df_feature = df_pivot[feature_colunms].copy()
        
        scaler = MinMaxScaler()
        scaled_hours = scaler.fit_transform(df_hours)
        from joblib import dump
        dump(scaler, 'NN/scaler.joblib')
        
        #将归一化后的数据转化为DateFrame
        df_hours_scaled = pd.DataFrame(scaled_hours,columns=hours_columns,index=df_hours.index)
        
        #合并
        df_pivot_clean = pd.concat([df_hours_scaled,df_feature],axis=1)
        print(df_pivot_clean)
        
        df_pivot_clean.to_csv('gitee/NN/scenic_data.csv',index=False)
        
if __name__ == '__main__':
    mu = MysqlUtils()
    mu.get_scenic_data()
        